The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Instrumental Variables: Application and Limitations
Abstract
To correct for confounding, the method of instrumental variables (IV) has been proposed. Its use in medical literature is still rather limited because of unfamiliarity or inapplicability. By introducing the method in a nontechnical way, we show that IV in a linear model is quite easy to understand and easy to apply once an appropriate instrumental variable has been identified. We also point out some limitations of the IV estimator when the instrumental variable is only weakly correlated with the exposure. The IV estimator will be imprecise (large standard error), biased when sample size is small, and biased in large samples when one of the assumptions is only slightly violated. For these reasons, it is advised to use an IV that is strongly correlated with exposure. However, we further show that under the assumptions required for the validity of the method, this correlation between IV and exposure is limited. Its maximum is low when confounding is strong, such as in case of confounding by indication. Finally, we show that in a study in which strong confounding is to be expected and an IV has been used that is moderately or strongly related to exposure, it is likely that the assumptions of IV are violated, resulting in a biased effect estimate. We conclude that instrumental variables can be useful in case of moderate confounding but are less useful when strong confounding exists, because strong instruments cannot be found and assumptions will be easily violated.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“IV analyses are an alternative estimation method that can be used to derive an estimate of the causal effect of the treatment (here, CRP) on the outcome (SBP) by accounting for the size of the association between randomization and CRP.3,4,12–15”
From Mendelian randomization (2022) · cites this paper as [13]
Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. H. (1997) · Econometrica · doi:10.2307/2171753 · Cited by 7,081
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. (1994) · doi:10.3386/t0151 · Cited by 5,302 · cited 4×
“However, it has been argued that the application of this method is limited because of its strong assumptions, making it difficult in practice to find a suitable instrumental variable.(16) The objectives of this article are first to introduce the application of the method of IV in epidemiology in a nontechnical way and…”
BackgroundMethodInfluentialIdentification of Causal Effects Using Instrumental Variables
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476902 · Cited by 4,230
Bound, J., Jaeger, D. A., & Baker, R. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476536 · Cited by 3,898
(1947) · The American Statistician · doi:10.1080/00031305.1947.10501534 · Cited by 3,745
Randomized, Controlled Trials, Observational Studies, and the Hierarchy of Research Designs
Concato, J., Shah, N., & Horwitz, R. I. (2000) · New England Journal of Medicine · doi:10.1056/nejm200006223422507 · Cited by 3,563
Hannan, E. J., & Theil, H. (1973) · Technometrics · doi:10.2307/1266838 · Cited by 3,238
Cox, D. R. (2008) · Springer eBooks · doi:10.1007/978-0-387-32833-1_5 · Cited by 2,687
Angrist, J. D., & Imbens, G. W. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476535 · Cited by 1,346
Mendelian randomization: prospects, potentials, and limitations
Smith, G. D. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh132 · Cited by 1,222
An introduction to instrumental variables for epidemiologists
Greenland, S. (2000) · International Journal of Epidemiology · doi:10.1093/ije/29.4.722 · Cited by 1,156
Angrist, J. D., & Imbens, G. W. (1995) · Journal of the American Statistical Association · doi:10.2307/2291054 · Cited by 917
Does More Intensive Treatment of Acute Myocardial Infarction in the Elderly Reduce Mortality?
McClellan, M. (1994) · JAMA · doi:10.1001/jama.1994.03520110039026 · Cited by 777
Split-Sample Instrumental Variables Estimates of the Return to Schooling
Angrist, J. D., & Krueger, A. B. (1995) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1995.10524597 · Cited by 417
Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator
Nelson, C. R., & Startz, R. (1990) · Econometrica · doi:10.2307/2938359 · Cited by 407
The nonlinear two-stage least-squares estimator
Amemiya, T. (1974) · Journal of Econometrics · doi:10.1016/0304-4076(74)90033-5 · Cited by 378
APOUPOPROTEIN E ISOFORMS, SERUM CHOLESTEROL, AND CANCER
Katan, M. (1986) · The Lancet · doi:10.1016/s0140-6736(86)92972-7 · Cited by 371
Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator
Nelson, C., & Startz, R. (1988) · National Bureau of Economic Research · doi:10.3386/t0068 · Cited by 342
McClellan, M., McNeil, B. J., & Newhouse, J. P. (1995) · Survey of Anesthesiology · doi:10.1097/00132586-199506000-00003 · Cited by 332
Earle, C. C., Tsai, J. S., Gelber, R. D., Weinstein, M. C., Neumann, P. J., & Weeks, J. C. (2001) · Journal of Clinical Oncology · doi:10.1200/jco.2001.19.4.1064 · Cited by 248
VIRUS-INFECTION OF URINARY-TRACT
Brouwers, T. M., Fontein, D. L., & Value, N. (1974) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 236
Methods to assess intended effects of drug treatment in observational studies are reviewed.
Klungel, O., Martens, E., Psaty, B., Grobbee, D., Sullivan, S., Stricker, B. H. C., Leufkens, H., & de Boer, A. (2004) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.03.011 · Cited by 220
Self-citationCommentary: The concept of 'Mendelian Randomization'
Thomas, D. C. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh048 · Cited by 217
Simultaneous-Equation Estimation in a Clinical Trial of the Effect of Smoking on Birth Weight
Permutt, T., & Hebel, J. R. (1989) · Biometrics · doi:10.2307/2531503 · Cited by 163
McMahon, A. (2003) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.883 · Cited by 140
The Exact Sampling Distribution of Ordinary Least Squares and Two-Stage Least Squares Estimators
Sawa, T. (1969) · Journal of the American Statistical Association · doi:10.1080/01621459.1969.10501024 · Cited by 138
A Comment on the Coefficient of Determination for Binary Responses
Cox, D. R., & Wermuth, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475836 · Cited by 135
Brooks, J. M., Chrischilles, E. A., Scott, S. D., & Chen-Hardee, S. (2003) · Health Services Research · doi:10.1111/j.1475-6773.2003.00184.x · Cited by 81
Minelli, C. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh228 · Cited by 78
Zohoori, N., & Savitz, D. A. (1997) · Annals of Epidemiology · doi:10.1016/s1047-2797(97)00023-9 · Cited by 74
Hadley, J., Polsky, D. E., Mandelblatt, J. S., Mitchell, J. M., Weeks, J. C., Wang, Q., & Hwang, Y. (2003) · Health Economics · doi:10.1002/hec.710 · Cited by 56
Leigh, J. P., & Schembri, M. E. (2004) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2003.08.006 · Cited by 51
Split Sample Instrumental Variables
Angrist, J. D., & Krueger, A. B. (1995) · National Bureau of Economic Research · doi:10.3386/t0150 · Cited by 36
Beck, C. A., Penrod, J. R., Gyorkos, T. W., Shapiro, S., & Pilote, L. (2003) · Health Services Research · doi:10.1111/j.1475-6773.2003.00186.x · Cited by 26
A Comparative Study of Instrumental Variables Estimators for Nonlinear Simultaneous Models
Bowden, R. J. J., & Turkington, D. A. (1981) · Journal of the American Statistical Association · doi:10.1080/01621459.1981.10477753 · Cited by 21
Explaining heterogeneity in meta-analysis: a comparison of methods
Thompson, S. G., & Sharp, S. J. (1999) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19991030)18:20
Research works printed but not linked: 1
- Bound, 1995, JASA, 90, 443
Cited by
Sorted by citations
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922
“The following three assumptions are necessary for a genetic variant to be a valid IV (Martens et al., 2006): IV1: the variant is predictive of the exposure; IV2: the variant is independent of any confounding factors of the exposure—outcome association; IV3: the variant is conditionally independent…”
BackgroundInfluentialAvoiding bias from weak instruments in Mendelian randomization studies
Burgess, S., Thompson, S. G., & CRP CHD Genetics Collaboration (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr036 · Cited by 3,952
Roux, A. V. D., & Mair, C. (2010) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.05333.x · Cited by 2,922
Mendelian Randomization as an Approach to Assess Causality Using Observational Data
Sekula, P., Del Greco M, F., Pattaro, C., & Köttgen, A. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2016010098 · Cited by 2,351
Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229
Metadata
- Authors
- Martens, E. P., Pestman, W. R., de Boer, A., Belitser, S. V., & Klungel, O. H.
- Affiliations
- Utrecht University (NL)
- Corresponding author
- Martens, Edwin P.
- Belitser, Svetlana V.
- Klungel, Olaf H.
- Venue
- Epidemiology
- Published
- 2006-05
- Volume
- 17
- Issue
- 3
- Pages
- 260-267
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2127065386
- Cited by
- 464
- References
- 37
- Citations by year
- 2026: 19 · 2025: 35 · 2024: 31 · 2023: 29 · 2022: 35 · 2021: 29 · 2020: 26 · 2019: 26 · 2018: 21 · 2017: 22 · 2016: 18 · 2015: 28 · 2014: 24 · 2013: 22 · 2012: 27
- FWCI
- 10.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- instrumental variables, confounding, confounding by indication, weak instruments, linear regression, exposure measurement error
- MeSH terms
- United States, Models, Statistical, Bias, Confounding Factors, Epidemiologic, Epidemiologic Studies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
